Key result
A machine learning diagnostic model using random forest and artificial neural network algorithms achieved an area under the curve of 0.757 in the training model and 0.685 in the test cohort for MDD.
Why the study?
Identifying new biomarkers of major depressive disorder would be of great significance for its early diagnosis and treatment.
Can a machine learning model based on transcriptomic data accurately diagnose major depressive disorder?
Observational
Can a machine learning model based on transcriptomic data accurately diagnose major depressive disorder?
Effect estimate: AUC 0.757 (training), 0.685 (test)
A machine learning model based on peripheral blood transcriptomic data demonstrates potential as an adjunct diagnostic tool for major depressive disorder.
May support transcriptomic ML as MDD adjunct; hypothesis-generating and requires prospective validation.
Background Identifying new biomarkers of major depressive disorder (MDD) would be of great significance for its early diagnosis and treatment. Herein, we constructed a diagnostic model of MDD using machine learning methods. Methods The GSE98793 and GSE19738 datasets were obtained from the Gene Expression Omnibus database, and the limma R package was used to analyze differentially expressed genes (DEGs) in MDD patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify potential molecular functions and pathways. A protein-protein interaction network (PPI) was constructed, and hub genes were predicted. Random forest (RF) and artificial neural network (ANN) machine-learning algorithms were used to select variables and construct a robust diagnostic model. Results A total of 721 DEGs were identified in peripheral blood samples of patients with MDD. GO and KEGG analyses revealed that the DEGs were mainly enriched in cytokines, defense responses to viruses, responses to biotic stimuli, immune effector processes, responses to external biotic stimuli, and immune systems. A PPI network was constructed, and CytoHubba plugins were used to screen hub genes. Furthermore, a robust diagnostic model was established using a RF and ANN algorithm with an area under the curve of 0.757 for the training model and 0.685 for the test cohort. Conclusion We analyzed potential driver genes in patients with MDD and built a potential diagnostic model as an adjunct tool to assist psychiatrists in the clinical diagnosis and treatment of MDD.
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Liu et al. (2022) conducted an observational in Major depressive disorder. Machine learning diagnostic model (Random forest and artificial neural network) was evaluated on Diagnostic model performance (Area under the curve) (AUC 0.757 (training), 0.685 (test)). A machine learning diagnostic model using random forest and artificial neural network algorithms achieved an area under the curve of 0.757 in the training model and 0.685 in the test cohort for MDD.
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